This application relates to inventory management systems.
In current inventory management systems, container induction is often based on a manual inference of scattered, insufficient, and non-integrated data points. This can lead to bottlenecks and poor distribution of resources. Even “autonomous” systems are often still semi-automated at best and only provide a snapshot of containers in the system at a given time. The operators then have to interpret and use their judgment to manually select containers and induct what seems to be the most appropriate one at the time. For instance, operators often base decisions on a fixed, first pick logic (which is typically based on a fixed sequence) with limited visibility of current system load and no visibility of future system load.
These manual induction decisions are frequently incorrect and lead to sub-optimal flow (e.g., bottlenecks, lower than optimal productivity, low utilization and throughput, higher than optimal cycle time) because of a lack of visibility into the system. For instance, container induction in these systems occurs with little or no understanding of its impact on the downstream labor impact of the operation.